Papers
8
Total Citations
80
H-Index
5
About
Fen Fang is a robotics and computer vision researcher whose work spans robot manipulation, active perception, and human-robot collaboration. With a growing body of highly cited publications, Fang has established herself as a contributor to some of the most challenging problems in intelligent robotics. Her most recognized work explores visuo-tactile feedback for robot manipulation (24 citations), combining vision and touch to navigate perceptual uncertainty in tasks like object packing — a frontier challenge in dexterous robotics. Complementing this, her research on self-supervised reinforcement learning for active object detection (19 citations) advances how robots autonomously determine optimal viewpoints, significantly expanding their perceptual capabilities without heavy human supervision. Fang's contributions to active vision are particularly notable, with multiple works addressing efficient multi-step view planning and adaptive action prediction for object detection in unstructured environments. Her earlier work on self-teaching strategies for collaborative robots demonstrates a consistent interest in reducing costly manual annotation, enabling robots to learn novel objects more independently. More recently, her application of diffusion models to surgical video procedure planning signals an exciting expansion into medical robotics. Across roughly 80 cumulative citations, Fang's research consistently bridges perception, learning, and real-world robotic application.
Research Focus
Key Achievements
Top Papers
- 1Visuo-Tactile Feedback-Based Robot Manipulation for Object Packing24 citations · 2023
- 2Self-Supervised Reinforcement Learning for Active Object Detection19 citations · 2022
- 3Towards Efficient Multiview Object Detection with Adaptive Action Prediction10 citations · 2021
- 4Enhancing Multi-Step Action Prediction for Active Object Detection8 citations · 2021
- 5Active Image Sampling on Canonical Views for Novel Object Detection7 citations · 2020
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